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Background: The efficacy and prognosis of major depressive disorder (MDD) are limited by its heterogeneity. MDD with melancholic features is an important subtype of MDD. The present study aimed to reveal the white matter (WM) network changes in melancholic depression., Materials and Methods: Twenty-three first-onset, untreated melancholic MDD, 59 non-melancholic MDD patients and 63 health controls underwent diffusion tensor imaging (DTI) scans. WM network analysis based on graph theory and support vector machine (SVM) were used for image data analysis., Results: Compared with HC, small-worldness was reduced and abnormal node attributes were in the right orbital inferior frontal gyrus, left orbital superior frontal gyrus, right caudate nucleus, right orbital superior frontal gyrus, right orbital middle frontal gyrus, left rectus gyrus, and left median cingulate and paracingulate gyrus of MDD patients. Compared with non-melancholic MDD, small-worldness was reduced and abnormal node attributes were in right orbital inferior frontal gyrus, left orbital superior frontal gyrus and right caudate nucleus of melancholic MDD. For correlation analysis, the 7th item score of the HRSD-17 (work and interest) was positively associated with increased node betweenness centrality (aBC) values in right orbital inferior frontal gyrus, while negatively associated with the decreased aBC in left orbital superior frontal gyrus. SVM analysis results showed that abnormal aBC in right orbital inferior frontal gyrus and left orbital superior frontal gyrus showed the highest accuracy of 81.0% (69/83), the sensitivity of 66.3%, and specificity of 85.2% for discriminating MDD patients with or without melancholic features., Conclusion: There is a significant difference in WM network changes between MDD patients with and without melancholic features., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 He, Cheng, Chu, Wang, Xu, Lu, Shen and Xu.)

Objective: Adult patients with major depressive disorder (MDD) may not actively reveal their suicidal ideation (SI). Therefore, this study is committed to finding the alterations in the cingulo-opercular network (CON) that are closely related to SI with multi-imaging methods, thus providing neuroimaging basis for SI., Method: A total of 198 participants (129 MDD patients and 69 healthy controls) were recruited and evaluated with the Montgomery-Asberg Depression Rating Scale (MADRS). The healthy individuals formed the HC group, while the MDD patients were subdivided into no SI MDD (NSI, n = 32), mild SI MDD (MSI, n = 64), and severe SI MDD (SSI, n = 33) according to their MADRS item 10. We obtained MRI data of all participants and applied regional homogeneity (ReHo) analysis to verify a previous finding that links CON abnormality to SI. In addition, we employed the structural covariance network (SCN) analysis to investigate the correlation between abnormal structural connectivity of CON and SI severity., Results: Compared to those of the HC group, MDD ReHo values and gray matter volume (GMV) were consistently found abnormal in CON. ReHo values and GMV of the right orbital inferior frontal gyrus (ORBinf.R) in the MDD group decreased with the increase of SI. Compared to the HC group, the MDD patients showed enhanced structural connectivity of three pairs of brain regions in CON [ACC.L-left superior frontal gyrus (SFG.L), SFG.L-left middle temporal gyrus (MTG.L), and the SFG.L-left post-central gyrus (PoCG.L)]. Compared with that of the NSI and MSI groups, the structural connectivity of three pairs of brain regions in CON is enhanced in the SSI groups [ORBinf.L-right ventral posterior cingulate gyrus (VPCC.R), VPCC.R-SFG.R, and SFG.R-PoCG.R]., Conclusion: Our findings showed the distinctive ReHo, GMV, and SCN pattern of CON in MDD patients with SI; and with the severity of suicide, abnormal brain regions increased. Our finding suggested that MDD patients with different severity of SI have different neuroimaging changes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 He, Ping, Chu, Zeng, Shen and Xu.)

5,10-Methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism is considered as a predisposition and promising genetic candidate to major depressive disorder (MDD), as it is associated with impaired one-carbon cycles, which may be involved in the pathogenesis of depression. Cortical thickness (CT) and subcortical structure volumes have been extensively studied in MDD and have been proposed as one of the phenotypes for MDD. We intend to discuss the association between CT, subcortical structure volume, and MTHFR C677T polymorphism in first-episode, treatment-naive patients with MDD. In this study, 127 adult patients with MDD and 101 age- and gender-matched healthy controls (HCs) were included. All subjects underwent T1-weighted MRI, MTHFR C677T genotyping, and FreeSurfer software-based morphological analysis. MDD patients have been detected to have significantly decreased volumes in the left nucleus accumbens ( P < 0.001). The MTHFR 677 T allele carriers manifested with thinner CT in the left caudal anterior cingulate cortex (cACC, P = 0.009) compared with CC genotype. There were significant genotype-by-diagnosis interactions for the CT in the left cACC ( P = 0.009), isthmus cingulate ( P = 0.002), medial orbitofrontal lobe ( P = 0.012), posterior cingulate ( P = 0.030), and the right lateral orbitofrontal lobe ( P = 0.012). We also found a trend in the interaction effect on the volume of the left putamen ( P = 0.050). Our results revealed that MTHFR C677T polymorphism may be involved in the dysfunction of limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuits mediating emotion processing, which may contribute to pathogenesis of MDD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Li, He, Xu, Dai, Ping, Zhou, Shen, Xu and Cheng.)

Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.